Customer Data & Analytics Blog

Last week’s blog post broke down how to evaluate your data stack, and what to expect from the process of integrating new AI technology into your MarTech stack. Today’s blog will talk about the results that AI will give you for running your stack more efficiently and accurately. 

AI technologies help to make your marketing stack more efficient and robust, and create less strain on resources than more traditional analytic technologies. That much is well-established among the global community of marketers. But on implementing a new system, what, specifically, should you be looking for in the ways of improvements, and how can you best represent these metrics in reports? AI improves your technologies across a whole host of areas—it can help make your department more agile, can help your company recognize greater revenue, and can generate deeper intelligence for your business.

Once you’ve identified specific problems you’d like to fix within your martech stack, you need to look into your data. Insights are powered by data, and data is necessitated by insights. You need to have the right amount of the right kinds of data to truly see the benefits you want out of AI technology.

A/B testing is an incredibly powerful and widely-used methodology to figure out what resonates with your customers. At a basic level, A/B testing is a statistical trial of sorts that compares two subjects in real-time. Does layout B increase time on our website? Does button color A or button color B generate more clicks? These questions and others can be used to help compile some comparative metrics for your team. Because the two trials are run concurrently and on randomized populations, marketing and data science teams can control for other variables that might affect the results.

Last week’s blog post focused on what AI is, what it can do within an organization, and its potential limitations. Today’s blog will talk about how you can go about finding the right technology for your business.

So you’ve done your research, you know what AI can do for your organization, and what its possible limitations are. You’ve found problems in your martech stack that you want to fix—maybe your data is siloed and it’s difficult to run a coordinated campaign, or marketing analytics are marred by human processing time and don’t scale well to the current size of your business. Whatever the case, you’ve decided to take the next step in integrating AI into your marketing stack.

Businesses of all sizes can employ machine learning to power their applications and services. AI can work across a handful of areas, from automatic segmentation to fraud prevention chatbot services. But there’s a new kid on the block that’s stealing the show: AutoML (Automated Machine Learning).